Overview

Dataset statistics

Number of variables15
Number of observations244
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.7 KiB
Average record size in memory120.5 B

Variable types

Numeric9
Categorical6

Alerts

year has constant value "2012" Constant
DC has a high cardinality: 198 distinct values High cardinality
FWI has a high cardinality: 126 distinct values High cardinality
day is highly correlated with DMC and 1 other fieldsHigh correlation
Temperature is highly correlated with month and 6 other fieldsHigh correlation
RH is highly correlated with Temperature and 3 other fieldsHigh correlation
Rain is highly correlated with Temperature and 3 other fieldsHigh correlation
FFMC is highly correlated with Temperature and 6 other fieldsHigh correlation
DMC is highly correlated with day and 5 other fieldsHigh correlation
ISI is highly correlated with Temperature and 5 other fieldsHigh correlation
BUI is highly correlated with day and 5 other fieldsHigh correlation
month is highly correlated with Temperature and 2 other fieldsHigh correlation
year is highly correlated with month and 2 other fieldsHigh correlation
Region is highly correlated with RHHigh correlation
Classes is highly correlated with Temperature and 5 other fieldsHigh correlation
Ws is highly correlated with Temperature and 1 other fieldsHigh correlation
DC is uniformly distributed Uniform
Region is uniformly distributed Uniform
Rain has 133 (54.5%) zeros Zeros
ISI has 4 (1.6%) zeros Zeros

Reproduction

Analysis started2022-10-02 05:55:43.889997
Analysis finished2022-10-02 05:56:14.744284
Duration30.85 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.75409836
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:15.219333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.825059228
Coefficient of variation (CV)0.5601754557
Kurtosis-1.198650172
Mean15.75409836
Median Absolute Deviation (MAD)8
Skewness0.002806412611
Sum3844
Variance77.88167038
MonotonicityNot monotonic
2022-10-02T11:26:15.423216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18
 
3.3%
178
 
3.3%
308
 
3.3%
298
 
3.3%
288
 
3.3%
278
 
3.3%
268
 
3.3%
258
 
3.3%
248
 
3.3%
238
 
3.3%
Other values (21)164
67.2%
ValueCountFrequency (%)
18
3.3%
28
3.3%
38
3.3%
48
3.3%
58
3.3%
68
3.3%
78
3.3%
88
3.3%
98
3.3%
108
3.3%
ValueCountFrequency (%)
314
1.6%
308
3.3%
298
3.3%
288
3.3%
278
3.3%
268
3.3%
258
3.3%
248
3.3%
238
3.3%
228
3.3%

month
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
7
62 
8
62 
6
60 
9
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Length

2022-10-02T11:26:15.625101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T11:26:15.920668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring characters

ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number244
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2012
244 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters976
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012244
100.0%

Length

2022-10-02T11:26:16.088101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T11:26:16.254005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2012244
100.0%

Most occurring characters

ValueCountFrequency (%)
2488
50.0%
0244
25.0%
1244
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2488
50.0%
0244
25.0%
1244
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2488
50.0%
0244
25.0%
1244
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2488
50.0%
0244
25.0%
1244
25.0%

Temperature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.17213115
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:16.393927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.85
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.63384326
Coefficient of variation (CV)0.1129500325
Kurtosis-0.1543103757
Mean32.17213115
Median Absolute Deviation (MAD)3
Skewness-0.1963088795
Sum7850
Variance13.20481684
MonotonicityNot monotonic
2022-10-02T11:26:16.592829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3529
11.9%
3125
10.2%
3424
9.8%
3323
9.4%
3022
9.0%
3221
8.6%
3621
8.6%
2918
7.4%
2815
6.1%
379
 
3.7%
Other values (9)37
15.2%
ValueCountFrequency (%)
222
 
0.8%
243
 
1.2%
256
 
2.5%
265
 
2.0%
278
 
3.3%
2815
6.1%
2918
7.4%
3022
9.0%
3125
10.2%
3221
8.6%
ValueCountFrequency (%)
421
 
0.4%
403
 
1.2%
396
 
2.5%
383
 
1.2%
379
 
3.7%
3621
8.6%
3529
11.9%
3424
9.8%
3323
9.4%
3221
8.6%

RH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.93852459
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:16.825696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152
median63
Q373.25
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation14.88420018
Coefficient of variation (CV)0.2403060176
Kurtosis-0.5303278714
Mean61.93852459
Median Absolute Deviation (MAD)11
Skewness-0.2379643933
Sum15113
Variance221.5394151
MonotonicityNot monotonic
2022-10-02T11:26:17.070555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410
 
4.1%
5510
 
4.1%
588
 
3.3%
548
 
3.3%
788
 
3.3%
687
 
2.9%
667
 
2.9%
737
 
2.9%
807
 
2.9%
657
 
2.9%
Other values (52)165
67.6%
ValueCountFrequency (%)
211
 
0.4%
241
 
0.4%
261
 
0.4%
291
 
0.4%
311
 
0.4%
332
0.8%
343
1.2%
351
 
0.4%
361
 
0.4%
374
1.6%
ValueCountFrequency (%)
901
 
0.4%
893
1.2%
883
1.2%
874
1.6%
863
1.2%
842
 
0.8%
831
 
0.4%
823
1.2%
816
2.5%
807
2.9%

Ws
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.50409836
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:17.291412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.810178371
Coefficient of variation (CV)0.181253905
Kurtosis2.602155825
Mean15.50409836
Median Absolute Deviation (MAD)2
Skewness0.5458812499
Sum3783
Variance7.897102476
MonotonicityNot monotonic
2022-10-02T11:26:17.481319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1443
17.6%
1540
16.4%
1330
12.3%
1728
11.5%
1627
11.1%
1826
10.7%
1915
 
6.1%
218
 
3.3%
117
 
2.9%
127
 
2.9%
Other values (8)13
 
5.3%
ValueCountFrequency (%)
61
 
0.4%
81
 
0.4%
92
 
0.8%
103
 
1.2%
117
 
2.9%
127
 
2.9%
1330
12.3%
1443
17.6%
1540
16.4%
1627
11.1%
ValueCountFrequency (%)
291
 
0.4%
261
 
0.4%
222
 
0.8%
218
 
3.3%
202
 
0.8%
1915
 
6.1%
1826
10.7%
1728
11.5%
1627
11.1%
1540
16.4%

Rain
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7606557377
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:17.685184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.355
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.999405565
Coefficient of variation (CV)2.628528868
Kurtosis25.94212272
Mean0.7606557377
Median Absolute Deviation (MAD)0
Skewness4.579070596
Sum185.6
Variance3.997622614
MonotonicityNot monotonic
2022-10-02T11:26:17.908058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0133
54.5%
0.118
 
7.4%
0.212
 
4.9%
0.310
 
4.1%
0.48
 
3.3%
0.76
 
2.5%
0.66
 
2.5%
0.55
 
2.0%
1.13
 
1.2%
1.23
 
1.2%
Other values (29)40
 
16.4%
ValueCountFrequency (%)
0133
54.5%
0.118
 
7.4%
0.212
 
4.9%
0.310
 
4.1%
0.48
 
3.3%
0.55
 
2.0%
0.66
 
2.5%
0.76
 
2.5%
0.82
 
0.8%
0.91
 
0.4%
ValueCountFrequency (%)
16.81
0.4%
13.11
0.4%
10.11
0.4%
8.71
0.4%
8.31
0.4%
7.21
0.4%
6.51
0.4%
61
0.4%
5.81
0.4%
4.71
0.4%

FFMC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.88770492
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:18.152915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.145
Q172.075
median83.5
Q388.3
95-th percentile92.185
Maximum96
Range67.4
Interquartile range (IQR)16.225

Descriptive statistics

Standard deviation14.33757088
Coefficient of variation (CV)0.1840800277
Kurtosis1.05520829
Mean77.88770492
Median Absolute Deviation (MAD)5.7
Skewness-1.325633262
Sum19004.6
Variance205.5659387
MonotonicityNot monotonic
2022-10-02T11:26:18.392777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.98
 
3.3%
89.45
 
2.0%
89.34
 
1.6%
85.44
 
1.6%
89.14
 
1.6%
78.33
 
1.2%
88.13
 
1.2%
88.33
 
1.2%
47.43
 
1.2%
79.93
 
1.2%
Other values (163)204
83.6%
ValueCountFrequency (%)
28.61
0.4%
30.51
0.4%
36.11
0.4%
37.31
0.4%
37.91
0.4%
40.91
0.4%
41.11
0.4%
42.61
0.4%
44.91
0.4%
451
0.4%
ValueCountFrequency (%)
961
0.4%
94.31
0.4%
94.21
0.4%
93.92
0.8%
93.81
0.4%
93.71
0.4%
93.31
0.4%
931
0.4%
92.52
0.8%
92.22
0.8%

DMC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct166
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.67336066
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:18.639654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.75
95-th percentile41.01
Maximum65.9
Range65.2
Interquartile range (IQR)14.95

Descriptive statistics

Standard deviation12.36803873
Coefficient of variation (CV)0.8428906658
Kurtosis2.487598085
Mean14.67336066
Median Absolute Deviation (MAD)6.9
Skewness1.527652386
Sum3580.3
Variance152.9683821
MonotonicityNot monotonic
2022-10-02T11:26:18.874501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95
 
2.0%
12.54
 
1.6%
1.94
 
1.6%
3.43
 
1.2%
4.63
 
1.2%
163
 
1.2%
63
 
1.2%
3.23
 
1.2%
9.73
 
1.2%
2.63
 
1.2%
Other values (156)210
86.1%
ValueCountFrequency (%)
0.71
 
0.4%
0.92
0.8%
1.12
0.8%
1.21
 
0.4%
1.33
1.2%
1.71
 
0.4%
1.94
1.6%
2.11
 
0.4%
2.22
0.8%
2.41
 
0.4%
ValueCountFrequency (%)
65.91
0.4%
61.31
0.4%
56.31
0.4%
54.21
0.4%
51.31
0.4%
50.21
0.4%
471
0.4%
46.61
0.4%
46.11
0.4%
45.61
0.4%

DC
Categorical

HIGH CARDINALITY
UNIFORM

Distinct198
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
8
 
5
7.6
 
4
7.8
 
4
8.4
 
4
7.5
 
4
Other values (193)
223 

Length

Max length6
Median length4
Mean length3.692622951
Min length1

Characters and Unicode

Total characters901
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)68.9%

Sample

1st row7.6
2nd row7.6
3rd row7.1
4th row6.9
5th row14.2

Common Values

ValueCountFrequency (%)
85
 
2.0%
7.64
 
1.6%
7.84
 
1.6%
8.44
 
1.6%
7.54
 
1.6%
8.34
 
1.6%
8.24
 
1.6%
173
 
1.2%
16.62
 
0.8%
102
 
0.8%
Other values (188)208
85.2%

Length

2022-10-02T11:26:19.118364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
85
 
2.0%
7.84
 
1.6%
8.44
 
1.6%
7.54
 
1.6%
8.34
 
1.6%
8.24
 
1.6%
7.64
 
1.6%
173
 
1.2%
30.52
 
0.8%
14.72
 
0.8%
Other values (189)209
85.3%

Most occurring characters

ValueCountFrequency (%)
.217
24.1%
1113
12.5%
792
10.2%
870
 
7.8%
468
 
7.5%
368
 
7.5%
268
 
7.5%
567
 
7.4%
657
 
6.3%
949
 
5.4%
Other values (2)32
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number683
75.8%
Other Punctuation217
 
24.1%
Space Separator1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1113
16.5%
792
13.5%
870
10.2%
468
10.0%
368
10.0%
268
10.0%
567
9.8%
657
8.3%
949
7.2%
031
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common901
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.217
24.1%
1113
12.5%
792
10.2%
870
 
7.8%
468
 
7.5%
368
 
7.5%
268
 
7.5%
567
 
7.4%
657
 
6.3%
949
 
5.4%
Other values (2)32
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.217
24.1%
1113
12.5%
792
10.2%
870
 
7.8%
468
 
7.5%
368
 
7.5%
268
 
7.5%
567
 
7.4%
657
 
6.3%
949
 
5.4%
Other values (2)32
 
3.6%

ISI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct106
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.774180328
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:19.333255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.3
95-th percentile13.37
Maximum19
Range19
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.175318098
Coefficient of variation (CV)0.8745622937
Kurtosis0.7833551712
Mean4.774180328
Median Absolute Deviation (MAD)2.4
Skewness1.121975461
Sum1164.9
Variance17.43328122
MonotonicityNot monotonic
2022-10-02T11:26:19.612076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.18
 
3.3%
1.27
 
2.9%
0.45
 
2.0%
4.75
 
2.0%
5.25
 
2.0%
1.55
 
2.0%
2.85
 
2.0%
15
 
2.0%
5.65
 
2.0%
2.24
 
1.6%
Other values (96)190
77.9%
ValueCountFrequency (%)
04
1.6%
0.14
1.6%
0.24
1.6%
0.33
1.2%
0.45
2.0%
0.52
 
0.8%
0.64
1.6%
0.74
1.6%
0.83
1.2%
0.92
 
0.8%
ValueCountFrequency (%)
191
0.4%
18.51
0.4%
17.21
0.4%
16.61
0.4%
161
0.4%
15.72
0.8%
15.51
0.4%
14.31
0.4%
14.21
0.4%
13.82
0.8%

BUI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct174
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.6647541
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T11:26:19.920899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.43
Q16
median12.25
Q322.525
95-th percentile46.35
Maximum68
Range66.9
Interquartile range (IQR)16.525

Descriptive statistics

Standard deviation14.20482398
Coefficient of variation (CV)0.8523872536
Kurtosis1.979149777
Mean16.6647541
Median Absolute Deviation (MAD)7.2
Skewness1.459068608
Sum4066.2
Variance201.7770242
MonotonicityNot monotonic
2022-10-02T11:26:20.191744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35
 
2.0%
5.14
 
1.6%
8.33
 
1.2%
7.73
 
1.2%
14.23
 
1.2%
22.43
 
1.2%
11.53
 
1.2%
10.93
 
1.2%
3.93
 
1.2%
2.93
 
1.2%
Other values (164)211
86.5%
ValueCountFrequency (%)
1.11
 
0.4%
1.42
0.8%
1.62
0.8%
1.72
0.8%
1.82
0.8%
2.21
 
0.4%
2.43
1.2%
2.62
0.8%
2.72
0.8%
2.82
0.8%
ValueCountFrequency (%)
681
0.4%
67.41
0.4%
641
0.4%
62.91
0.4%
59.51
0.4%
59.31
0.4%
57.11
0.4%
54.91
0.4%
54.71
0.4%
50.91
0.4%

FWI
Categorical

HIGH CARDINALITY

Distinct126
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0.4
 
12
0.8
 
10
0.5
 
9
0.1
 
9
0
 
9
Other values (121)
195 

Length

Max length7
Median length3
Mean length3.081967213
Min length1

Characters and Unicode

Total characters752
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)31.1%

Sample

1st row0.5
2nd row0.4
3rd row0.1
4th row0
5th row0.5

Common Values

ValueCountFrequency (%)
0.412
 
4.9%
0.810
 
4.1%
0.59
 
3.7%
0.19
 
3.7%
09
 
3.7%
0.38
 
3.3%
0.97
 
2.9%
0.26
 
2.5%
0.75
 
2.0%
0.64
 
1.6%
Other values (116)165
67.6%

Length

2022-10-02T11:26:20.474581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.412
 
4.9%
0.810
 
4.1%
0.59
 
3.7%
0.19
 
3.7%
09
 
3.7%
0.38
 
3.3%
0.97
 
2.9%
0.26
 
2.5%
0.75
 
2.0%
0.64
 
1.6%
Other values (116)165
67.6%

Most occurring characters

ValueCountFrequency (%)
.215
28.6%
096
12.8%
196
12.8%
261
 
8.1%
355
 
7.3%
541
 
5.5%
941
 
5.5%
738
 
5.1%
436
 
4.8%
636
 
4.8%
Other values (6)37
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number530
70.5%
Other Punctuation215
28.6%
Lowercase Letter4
 
0.5%
Space Separator3
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
096
18.1%
196
18.1%
261
11.5%
355
10.4%
541
7.7%
941
7.7%
738
 
7.2%
436
 
6.8%
636
 
6.8%
830
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
f1
25.0%
i1
25.0%
r1
25.0%
e1
25.0%
Other Punctuation
ValueCountFrequency (%)
.215
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common748
99.5%
Latin4
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
.215
28.7%
096
12.8%
196
12.8%
261
 
8.2%
355
 
7.4%
541
 
5.5%
941
 
5.5%
738
 
5.1%
436
 
4.8%
636
 
4.8%
Other values (2)33
 
4.4%
Latin
ValueCountFrequency (%)
f1
25.0%
i1
25.0%
r1
25.0%
e1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.215
28.6%
096
12.8%
196
12.8%
261
 
8.1%
355
 
7.3%
541
 
5.5%
941
 
5.5%
738
 
5.1%
436
 
4.8%
636
 
4.8%
Other values (6)37
 
4.9%

Classes
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)3.3%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
fire
131 
not fire
101 
fire
 
4
fire
 
2
not fire
 
2
Other values (3)
 
3

Length

Max length13
Median length7
Mean length8.658436214
Min length4

Characters and Unicode

Total characters2104
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.2%

Sample

1st rownot fire
2nd rownot fire
3rd rownot fire
4th rownot fire
5th rownot fire

Common Values

ValueCountFrequency (%)
fire 131
53.7%
not fire 101
41.4%
fire4
 
1.6%
fire 2
 
0.8%
not fire2
 
0.8%
not fire 1
 
0.4%
not fire 1
 
0.4%
not fire 1
 
0.4%
(Missing)1
 
0.4%

Length

2022-10-02T11:26:20.674466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T11:26:20.891361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
fire243
69.6%
not106
30.4%

Most occurring characters

ValueCountFrequency (%)
814
38.7%
f243
 
11.5%
i243
 
11.5%
r243
 
11.5%
e243
 
11.5%
n106
 
5.0%
o106
 
5.0%
t106
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1290
61.3%
Space Separator814
38.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f243
18.8%
i243
18.8%
r243
18.8%
e243
18.8%
n106
8.2%
o106
8.2%
t106
8.2%
Space Separator
ValueCountFrequency (%)
814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1290
61.3%
Common814
38.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
f243
18.8%
i243
18.8%
r243
18.8%
e243
18.8%
n106
8.2%
o106
8.2%
t106
8.2%
Common
ValueCountFrequency (%)
814
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
814
38.7%
f243
 
11.5%
i243
 
11.5%
r243
 
11.5%
e243
 
11.5%
n106
 
5.0%
o106
 
5.0%
t106
 
5.0%

Region
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
122 
0
122 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1122
50.0%
0122
50.0%

Length

2022-10-02T11:26:21.431032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T11:26:21.608930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1122
50.0%
0122
50.0%

Most occurring characters

ValueCountFrequency (%)
1122
50.0%
0122
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number244
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1122
50.0%
0122
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1122
50.0%
0122
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1122
50.0%
0122
50.0%

Interactions

2022-10-02T11:26:11.748846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:55.538061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:58.207592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:00.465327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:02.457185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:04.217171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:06.130072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:07.945050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:09.735006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:11.940737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:56.492596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:58.494429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:00.661215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:02.637101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:04.405066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:06.322964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:08.126929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:09.949898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:12.148616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:56.726443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:58.710302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:00.903095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:02.840962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:04.618940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:06.527844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:08.323813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:10.156760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:12.342509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:56.931330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:58.918235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:01.096967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:03.036849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:04.820826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:06.716755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:08.516702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:10.362642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:12.521405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:57.150201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:59.156081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:01.286855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:03.219744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:05.016731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:06.920619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:08.705594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:10.542538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:12.722287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:57.335092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:59.444912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:01.475746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:03.452610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:05.274564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:07.146489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:08.948454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:10.744422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:12.927188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:57.548988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:59.719758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:01.668635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:03.667487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:05.474452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:07.320389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:09.146343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:10.942308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:13.113063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:57.731867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:59.991600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:01.854529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:03.849383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:05.688332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:07.511283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:09.329235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:11.357072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:13.310951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:25:57.947740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:00.254447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:02.066407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:04.037277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:05.925210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:07.713186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:09.527122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T11:26:11.555956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-02T11:26:21.779851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T11:26:22.100666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T11:26:22.425479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T11:26:22.708317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-02T11:26:22.915182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T11:26:13.653751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T11:26:14.127498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-02T11:26:14.538403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegion
01620122957180.065.73.47.61.33.40.5not fire1
12620122961131.364.44.17.61.03.90.4not fire1
236201226822213.147.12.57.10.32.70.1not fire1
34620122589132.528.61.36.90.01.70not fire1
45620122777160.064.83.014.21.23.90.5not fire1
56620123167140.082.65.822.23.17.02.5fire1
67620123354130.088.29.930.56.410.97.2fire1
78620123073150.086.612.138.35.613.57.1fire1
89620122588130.252.97.938.80.410.50.3not fire1
910620122879120.073.29.546.31.312.60.9not fire1

Last rows

daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegion
23421920123534170.092.223.697.313.829.421.6fire0
23522920123364130.088.926.1106.37.132.413.7fire0
23623920123556140.089.029.4115.67.536.015.2fire0
2372492012264962.061.311.928.10.611.90.4not fire0
23825920122870150.079.913.836.12.414.13not fire0
23926920123065140.085.416.044.54.516.96.5fire0
24027920122887154.441.16.580.16.20not fire0
24128920122787290.545.93.57.90.43.40.2not fire0
24229920122454180.179.74.315.21.75.10.7not fire0
24330920122464150.267.33.816.51.24.80.5not fire0